In [28]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

Load the datasets downloaded from Kaggle¶

In [2]:
df_2022_train = pd.read_csv('../data/raw/2022_train.csv')
df_2022_test = pd.read_csv('../data/raw/2022_test.csv')
In [3]:
df_2022_train.head()
Out[3]:
Id GP MIN PTS FGM FGA FG% 3P Made 3PA 3P% ... FTA FT% OREB DREB REB AST STL BLK TOV TARGET_5Yrs
0 3799 80 24.3 7.8 3.0 6.4 45.7 0.1 0.3 22.6 ... 2.9 72.1 2.2 2.0 3.8 3.2 1.1 0.2 1.6 1
1 3800 75 21.8 10.5 4.2 7.9 55.1 -0.3 -1.0 34.9 ... 3.6 67.8 3.6 3.7 6.6 0.7 0.5 0.6 1.4 1
2 3801 85 19.1 4.5 1.9 4.5 42.8 0.4 1.2 34.3 ... 0.6 75.7 0.6 1.8 2.4 0.8 0.4 0.2 0.6 1
3 3802 63 19.1 8.2 3.5 6.7 52.5 0.3 0.8 23.7 ... 1.5 66.9 0.8 2.0 3.0 1.8 0.4 0.1 1.9 1
4 3803 63 17.8 3.7 1.7 3.4 50.8 0.5 1.4 13.7 ... 0.5 54.0 2.4 2.7 4.9 0.4 0.4 0.6 0.7 1

5 rows × 21 columns

In [4]:
df_2022_test.head()
Out[4]:
Id GP MIN PTS FGM FGA FG% 3P Made 3PA 3P% FTM FTA FT% OREB DREB REB AST STL BLK TOV
0 0 56 9.1 4.0 1.6 3.7 43.7 0.1 0.3 7.3 0.7 1.2 63.4 1.2 0.8 1.7 0.4 0.2 0.3 0.8
1 1 43 19.3 10.1 3.7 8.1 46.0 0.6 1.7 35.1 1.8 2.5 75.3 0.5 0.9 1.5 3.5 0.6 0.0 1.8
2 2 82 33.9 11.3 4.9 10.6 45.6 0.5 1.9 44.8 1.8 2.7 71.2 1.3 3.3 4.5 2.5 1.3 0.3 2.0
3 3 86 44.7 18.8 6.8 15.9 42.9 0.5 1.8 13.5 4.5 6.3 70.9 1.5 3.2 5.0 4.1 0.9 0.1 3.6
4 4 58 12.3 4.7 1.6 4.0 40.0 0.5 1.7 38.7 1.1 1.3 76.9 0.2 0.6 0.9 1.5 0.5 -0.4 0.9

1. Structure investigation¶

The raw train datasets have 8000 rows and 21 columns. The test data set have 3799 rows and 20 columns. The train data set have the target variable. They both have all columns numerical type.¶

In [5]:
df_2022_train.shape
Out[5]:
(8000, 21)
In [6]:
df_2022_test.shape
Out[6]:
(3799, 20)
In [7]:
df_2022_train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8000 entries, 0 to 7999
Data columns (total 21 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   Id           8000 non-null   int64  
 1   GP           8000 non-null   int64  
 2   MIN          8000 non-null   float64
 3   PTS          8000 non-null   float64
 4   FGM          8000 non-null   float64
 5   FGA          8000 non-null   float64
 6   FG%          8000 non-null   float64
 7   3P Made      8000 non-null   float64
 8   3PA          8000 non-null   float64
 9   3P%          8000 non-null   float64
 10  FTM          8000 non-null   float64
 11  FTA          8000 non-null   float64
 12  FT%          8000 non-null   float64
 13  OREB         8000 non-null   float64
 14  DREB         8000 non-null   float64
 15  REB          8000 non-null   float64
 16  AST          8000 non-null   float64
 17  STL          8000 non-null   float64
 18  BLK          8000 non-null   float64
 19  TOV          8000 non-null   float64
 20  TARGET_5Yrs  8000 non-null   int64  
dtypes: float64(18), int64(3)
memory usage: 1.3 MB
In [8]:
df_2022_test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3799 entries, 0 to 3798
Data columns (total 20 columns):
 #   Column   Non-Null Count  Dtype  
---  ------   --------------  -----  
 0   Id       3799 non-null   int64  
 1   GP       3799 non-null   int64  
 2   MIN      3799 non-null   float64
 3   PTS      3799 non-null   float64
 4   FGM      3799 non-null   float64
 5   FGA      3799 non-null   float64
 6   FG%      3799 non-null   float64
 7   3P Made  3799 non-null   float64
 8   3PA      3799 non-null   float64
 9   3P%      3799 non-null   float64
 10  FTM      3799 non-null   float64
 11  FTA      3799 non-null   float64
 12  FT%      3799 non-null   float64
 13  OREB     3799 non-null   float64
 14  DREB     3799 non-null   float64
 15  REB      3799 non-null   float64
 16  AST      3799 non-null   float64
 17  STL      3799 non-null   float64
 18  BLK      3799 non-null   float64
 19  TOV      3799 non-null   float64
dtypes: float64(18), int64(2)
memory usage: 593.7 KB

Create a copy of each of the data to preserve the original data. Rename the features into something more descriptive!¶

In [9]:
df_main = df_2022_train.rename(columns = {
    'GP':'Games Played', 
    'MIN':'Minutes Played',
    'PTS':'Points Per Game',
    'FGM' : 'Field Goals Made',
    'FGA' : 'Field Goals Attempts',
    'FG%' : 'Field Goals Percent',
    '3P Made' : '3Points Made',
    '3PA' : '3Points Attempts',
    '3P%' : '3Points Percent',
    'FTM' : 'Free Throw Made',
    'FTA' : 'Free Throw Attempts',
    'FT%' : 'Free Throw Percent',
    'OREB' : 'Offensive Rebounds',
    'DREB' : 'Defensive Rebounds',
    'REB' : 'Rebounds',
    'AST' : 'Assists',
    'STL' : 'Steals',
    'BLK' : 'Blocks',
    'TOV' : 'Turnovers'
    })

df_main.head(20)
Out[9]:
Id Games Played Minutes Played Points Per Game Field Goals Made Field Goals Attempts Field Goals Percent 3Points Made 3Points Attempts 3Points Percent ... Free Throw Attempts Free Throw Percent Offensive Rebounds Defensive Rebounds Rebounds Assists Steals Blocks Turnovers TARGET_5Yrs
0 3799 80 24.3 7.8 3.0 6.4 45.7 0.1 0.3 22.6 ... 2.9 72.1 2.2 2.0 3.8 3.2 1.1 0.2 1.6 1
1 3800 75 21.8 10.5 4.2 7.9 55.1 -0.3 -1.0 34.9 ... 3.6 67.8 3.6 3.7 6.6 0.7 0.5 0.6 1.4 1
2 3801 85 19.1 4.5 1.9 4.5 42.8 0.4 1.2 34.3 ... 0.6 75.7 0.6 1.8 2.4 0.8 0.4 0.2 0.6 1
3 3802 63 19.1 8.2 3.5 6.7 52.5 0.3 0.8 23.7 ... 1.5 66.9 0.8 2.0 3.0 1.8 0.4 0.1 1.9 1
4 3803 63 17.8 3.7 1.7 3.4 50.8 0.5 1.4 13.7 ... 0.5 54.0 2.4 2.7 4.9 0.4 0.4 0.6 0.7 1
5 3804 88 20.0 8.8 3.7 8.7 43.6 -0.2 -0.5 6.9 ... 2.8 75.1 1.8 2.9 4.7 1.8 0.4 0.3 1.1 1
6 3805 70 20.6 7.0 3.2 5.4 58.9 0.1 -0.1 -3.6 ... 1.4 56.7 2.9 4.6 7.6 0.6 0.4 0.7 1.2 1
7 3806 57 17.6 5.4 1.7 4.7 37.2 0.9 2.8 33.2 ... 1.8 78.3 0.8 1.7 2.6 0.4 0.6 0.6 0.3 1
8 3807 46 26.5 9.3 3.1 7.2 44.0 0.6 1.7 27.5 ... 2.3 82.1 1.3 2.8 4.1 1.8 0.7 0.6 1.7 1
9 3808 64 33.4 16.9 6.4 11.3 59.0 0.3 1.1 -13.0 ... 4.9 74.9 3.4 8.0 11.9 0.8 0.4 2.5 2.4 1
10 3809 72 30.6 13.2 4.9 11.0 46.9 1.0 2.5 48.1 ... 2.8 74.1 1.0 3.1 3.9 4.0 1.1 0.2 1.5 1
11 3810 47 15.2 6.5 2.5 6.5 38.3 0.7 2.5 39.7 ... 1.1 75.5 0.6 1.3 2.0 0.3 0.4 0.3 0.7 1
12 3811 41 9.8 2.3 0.9 2.3 37.9 -0.4 -0.6 -4.7 ... 1.3 64.7 1.5 1.9 3.5 0.2 0.3 0.4 0.4 0
13 3812 64 29.7 12.0 4.2 9.6 45.2 0.8 2.1 28.3 ... 3.3 76.8 1.5 3.1 4.8 3.8 1.5 0.0 2.0 1
14 3813 90 31.1 12.9 4.9 10.3 47.0 0.3 1.1 17.0 ... 2.6 93.4 1.6 3.4 4.7 2.3 0.6 0.4 2.5 1
15 3814 45 5.2 1.3 0.5 1.4 41.0 0.1 0.1 21.1 ... 0.2 73.3 0.3 0.7 1.0 0.2 0.2 0.2 0.5 1
16 3815 29 7.5 1.9 0.7 2.6 30.1 0.3 1.0 26.8 ... 0.4 68.0 0.4 0.8 1.2 0.5 0.3 0.1 0.7 1
17 3816 53 13.8 6.7 2.1 4.7 43.5 0.2 0.7 19.8 ... 3.8 75.1 1.1 1.9 3.1 0.2 0.4 0.6 0.7 1
18 3817 83 8.5 2.2 0.9 1.8 48.4 -0.3 -1.0 5.6 ... 0.9 63.4 0.2 0.6 0.8 1.3 0.4 0.2 0.8 1
19 3818 64 10.4 5.1 2.2 4.9 45.5 -0.1 -0.2 -6.1 ... 1.8 43.6 0.7 1.0 1.6 1.2 0.5 0.1 1.2 1

20 rows × 21 columns

In [10]:
df_test = df_2022_test.rename(columns = {
    'GP':'Games Played', 
    'MIN':'Minutes Played',
    'PTS':'Points Per Game',
    'FGM' : 'Field Goals Made',
    'FGA' : 'Field Goals Attempts',
    'FG%' : 'Field Goals Percent',
    '3P Made' : '3Points Made',
    '3PA' : '3Points Attempts',
    '3P%' : '3Points Percent',
    'FTM' : 'Free Throw Made',
    'FTA' : 'Free Throw Attempts',
    'FT%' : 'Free Throw Percent',
    'OREB' : 'Offensive Rebounds',
    'DREB' : 'Defensive Rebounds',
    'REB' : 'Rebounds',
    'AST' : 'Assists',
    'STL' : 'Steals',
    'BLK' : 'Blocks',
    'TOV' : 'Turnovers'
    })

df_test.head(20)
Out[10]:
Id Games Played Minutes Played Points Per Game Field Goals Made Field Goals Attempts Field Goals Percent 3Points Made 3Points Attempts 3Points Percent Free Throw Made Free Throw Attempts Free Throw Percent Offensive Rebounds Defensive Rebounds Rebounds Assists Steals Blocks Turnovers
0 0 56 9.1 4.0 1.6 3.7 43.7 0.1 0.3 7.3 0.7 1.2 63.4 1.2 0.8 1.7 0.4 0.2 0.3 0.8
1 1 43 19.3 10.1 3.7 8.1 46.0 0.6 1.7 35.1 1.8 2.5 75.3 0.5 0.9 1.5 3.5 0.6 0.0 1.8
2 2 82 33.9 11.3 4.9 10.6 45.6 0.5 1.9 44.8 1.8 2.7 71.2 1.3 3.3 4.5 2.5 1.3 0.3 2.0
3 3 86 44.7 18.8 6.8 15.9 42.9 0.5 1.8 13.5 4.5 6.3 70.9 1.5 3.2 5.0 4.1 0.9 0.1 3.6
4 4 58 12.3 4.7 1.6 4.0 40.0 0.5 1.7 38.7 1.1 1.3 76.9 0.2 0.6 0.9 1.5 0.5 -0.4 0.9
5 5 59 16.1 7.1 2.8 4.5 62.0 -0.1 -0.6 3.4 1.7 2.7 58.6 1.8 2.6 4.6 0.6 0.6 0.4 0.9
6 6 45 8.3 3.2 1.4 2.8 46.4 -0.5 -1.6 8.9 0.5 0.7 69.3 0.5 0.5 1.2 0.2 0.1 0.2 0.4
7 7 53 12.1 4.7 1.8 4.1 44.1 0.1 0.2 25.7 0.9 1.0 80.2 0.6 1.1 1.6 0.8 0.4 -0.2 0.6
8 8 41 21.6 7.9 3.1 6.0 49.6 0.3 0.9 -14.8 1.8 2.2 77.6 2.4 2.6 5.1 1.4 0.5 0.4 1.4
9 9 25 10.0 2.6 1.0 2.3 45.0 0.2 0.7 11.7 0.4 0.5 84.1 0.4 1.5 1.8 0.4 0.2 0.2 0.5
10 10 72 12.6 3.5 1.3 3.2 41.9 0.1 0.1 14.9 0.5 0.6 74.8 0.5 1.4 1.8 1.1 0.4 0.2 0.8
11 11 67 21.3 11.1 4.6 8.3 53.9 0.0 0.2 22.5 1.9 2.4 81.3 1.6 2.4 4.0 3.3 1.3 0.3 1.7
12 12 47 31.1 11.5 4.1 9.9 42.1 1.0 3.0 28.7 2.1 2.6 72.3 1.0 3.3 4.4 1.9 0.9 0.2 1.2
13 13 87 20.7 10.9 4.5 9.8 48.4 0.5 1.1 28.1 1.4 2.3 61.2 0.9 1.4 2.2 3.2 1.0 0.0 2.3
14 14 82 43.4 17.3 7.0 15.4 46.2 1.0 2.5 31.2 2.4 2.9 84.3 2.1 3.5 5.3 4.3 1.4 0.2 3.0
15 15 68 42.4 23.1 10.0 20.8 48.7 0.6 1.7 7.0 3.4 5.1 69.4 2.0 4.3 6.4 4.2 1.7 0.3 2.3
16 16 78 23.9 8.1 3.1 5.8 52.1 -0.1 -0.1 33.8 2.1 3.2 64.3 1.4 2.3 3.7 1.4 0.6 0.3 1.8
17 17 52 21.2 9.7 3.8 8.0 49.0 0.7 2.1 35.6 1.6 2.2 67.8 1.0 2.5 3.4 1.7 0.9 0.3 1.5
18 18 27 8.5 1.6 0.7 1.4 50.4 -0.2 -0.6 22.4 0.1 0.2 55.8 0.6 1.1 1.5 0.4 0.3 0.0 0.4
19 19 77 22.2 6.9 3.0 6.6 45.1 0.1 0.2 -8.2 1.0 1.6 49.2 2.2 2.3 4.1 1.4 0.4 0.6 1.5

2. Quality investigation¶

2.1 Duplicates¶

In [11]:
# Check number of duplicates
duplicates_count = df_main.duplicated().sum()
print(f"You have {duplicates_count} duplicates in the main data.")

You have 0 duplicates in the main data.
In [12]:
# Check number of duplicates while ignoring the index feature
duplicates_count = df_main.drop(labels=["Id"], axis=1).duplicated().sum()
print(f"You have {duplicates_count} duplicates in the main data - ignoring the Id.")

You have 0 duplicates in the main data - ignoring the Id.
In [13]:
# Check number of duplicates
duplicates_count = df_test.duplicated().sum()
print(f"You have {duplicates_count} duplicates in the test data.")

You have 0 duplicates in the test data.
In [14]:
# Check number of duplicates while ignoring the index feature
duplicates_count = df_test.drop(labels=["Id"], axis=1).duplicated().sum()
print(f"You have {duplicates_count} duplicates in the test data - ignoring the Id.")

You have 0 duplicates in the test data - ignoring the Id.

2.2 Missing values¶

In [15]:
df_main.describe()
Out[15]:
Id Games Played Minutes Played Points Per Game Field Goals Made Field Goals Attempts Field Goals Percent 3Points Made 3Points Attempts 3Points Percent ... Free Throw Attempts Free Throw Percent Offensive Rebounds Defensive Rebounds Rebounds Assists Steals Blocks Turnovers TARGET_5Yrs
count 8000.00000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 ... 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000
mean 7798.50000 62.777875 18.576662 7.267088 2.807037 6.231212 44.608900 0.264525 0.816562 19.583700 ... 1.947788 71.365825 1.077838 2.168500 3.245300 1.624513 0.648687 0.245212 1.257763 0.833625
std 2309.54541 17.118774 8.935263 4.318732 1.693373 3.584559 6.155453 0.384093 1.060964 16.003155 ... 1.252352 10.430447 0.785670 1.392224 2.085154 1.355986 0.407626 0.821037 0.723270 0.372440
min 3799.00000 -8.000000 2.900000 0.800000 0.300000 0.800000 21.300000 -1.100000 -3.100000 -38.500000 ... 0.000000 -13.300000 0.000000 0.200000 0.300000 0.000000 0.000000 -17.900000 0.100000 0.000000
25% 5798.75000 51.000000 12.000000 4.100000 1.600000 3.600000 40.400000 0.000000 0.100000 8.400000 ... 1.000000 65.000000 0.500000 1.100000 1.700000 0.700000 0.300000 0.100000 0.700000 1.000000
50% 7798.50000 63.000000 16.800000 6.300000 2.400000 5.400000 44.400000 0.300000 0.800000 19.500000 ... 1.700000 71.400000 0.900000 1.900000 2.800000 1.300000 0.600000 0.200000 1.100000 1.000000
75% 9798.25000 74.000000 23.500000 9.500000 3.700000 8.100000 48.700000 0.500000 1.500000 30.600000 ... 2.600000 77.500000 1.500000 2.900000 4.300000 2.200000 0.900000 0.400000 1.600000 1.000000
max 11798.00000 123.000000 73.800000 34.200000 13.100000 28.900000 67.200000 1.700000 4.700000 82.100000 ... 11.100000 168.900000 5.500000 11.000000 15.900000 12.800000 3.600000 18.900000 5.300000 1.000000

8 rows × 21 columns

In [16]:
df_main.describe()
Out[16]:
Id Games Played Minutes Played Points Per Game Field Goals Made Field Goals Attempts Field Goals Percent 3Points Made 3Points Attempts 3Points Percent ... Free Throw Attempts Free Throw Percent Offensive Rebounds Defensive Rebounds Rebounds Assists Steals Blocks Turnovers TARGET_5Yrs
count 8000.00000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 ... 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000 8000.000000
mean 7798.50000 62.777875 18.576662 7.267088 2.807037 6.231212 44.608900 0.264525 0.816562 19.583700 ... 1.947788 71.365825 1.077838 2.168500 3.245300 1.624513 0.648687 0.245212 1.257763 0.833625
std 2309.54541 17.118774 8.935263 4.318732 1.693373 3.584559 6.155453 0.384093 1.060964 16.003155 ... 1.252352 10.430447 0.785670 1.392224 2.085154 1.355986 0.407626 0.821037 0.723270 0.372440
min 3799.00000 -8.000000 2.900000 0.800000 0.300000 0.800000 21.300000 -1.100000 -3.100000 -38.500000 ... 0.000000 -13.300000 0.000000 0.200000 0.300000 0.000000 0.000000 -17.900000 0.100000 0.000000
25% 5798.75000 51.000000 12.000000 4.100000 1.600000 3.600000 40.400000 0.000000 0.100000 8.400000 ... 1.000000 65.000000 0.500000 1.100000 1.700000 0.700000 0.300000 0.100000 0.700000 1.000000
50% 7798.50000 63.000000 16.800000 6.300000 2.400000 5.400000 44.400000 0.300000 0.800000 19.500000 ... 1.700000 71.400000 0.900000 1.900000 2.800000 1.300000 0.600000 0.200000 1.100000 1.000000
75% 9798.25000 74.000000 23.500000 9.500000 3.700000 8.100000 48.700000 0.500000 1.500000 30.600000 ... 2.600000 77.500000 1.500000 2.900000 4.300000 2.200000 0.900000 0.400000 1.600000 1.000000
max 11798.00000 123.000000 73.800000 34.200000 13.100000 28.900000 67.200000 1.700000 4.700000 82.100000 ... 11.100000 168.900000 5.500000 11.000000 15.900000 12.800000 3.600000 18.900000 5.300000 1.000000

8 rows × 21 columns

In [17]:
df_test.describe()
Out[17]:
Id Games Played Minutes Played Points Per Game Field Goals Made Field Goals Attempts Field Goals Percent 3Points Made 3Points Attempts 3Points Percent Free Throw Made Free Throw Attempts Free Throw Percent Offensive Rebounds Defensive Rebounds Rebounds Assists Steals Blocks Turnovers
count 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000
mean 1899.000000 62.853909 18.650224 7.328034 2.835404 6.302580 44.599079 0.255962 0.796920 19.234746 1.399842 1.953567 71.612924 1.096025 2.179495 3.275783 1.636483 0.653593 0.257726 1.257910
std 1096.821164 17.151740 8.727259 4.294724 1.688427 3.579221 6.040168 0.380987 1.052862 15.968989 0.926140 1.250376 10.457336 0.785678 1.371935 2.070646 1.335496 0.410573 0.639660 0.712449
min 0.000000 6.000000 3.700000 0.700000 0.300000 0.800000 25.100000 -1.000000 -2.700000 -38.000000 0.000000 0.000000 23.700000 0.000000 0.200000 0.300000 0.000000 0.000000 -7.100000 0.100000
25% 949.500000 51.000000 12.200000 4.200000 1.600000 3.700000 40.500000 0.000000 0.100000 8.500000 0.700000 1.000000 65.000000 0.500000 1.200000 1.800000 0.600000 0.400000 0.100000 0.700000
50% 1899.000000 63.000000 17.000000 6.400000 2.500000 5.500000 44.600000 0.300000 0.800000 19.400000 1.200000 1.700000 71.500000 0.900000 1.900000 2.800000 1.300000 0.600000 0.200000 1.100000
75% 2848.500000 74.000000 23.300000 9.400000 3.700000 8.100000 48.500000 0.500000 1.500000 30.250000 1.900000 2.600000 78.000000 1.500000 2.900000 4.300000 2.300000 0.900000 0.400000 1.600000
max 3798.000000 126.000000 68.000000 33.000000 13.400000 26.200000 74.600000 1.600000 4.300000 73.800000 7.800000 9.800000 127.100000 6.900000 12.000000 18.500000 9.000000 2.700000 14.800000 5.200000
In [18]:
df_test.describe()
Out[18]:
Id Games Played Minutes Played Points Per Game Field Goals Made Field Goals Attempts Field Goals Percent 3Points Made 3Points Attempts 3Points Percent Free Throw Made Free Throw Attempts Free Throw Percent Offensive Rebounds Defensive Rebounds Rebounds Assists Steals Blocks Turnovers
count 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000 3799.000000
mean 1899.000000 62.853909 18.650224 7.328034 2.835404 6.302580 44.599079 0.255962 0.796920 19.234746 1.399842 1.953567 71.612924 1.096025 2.179495 3.275783 1.636483 0.653593 0.257726 1.257910
std 1096.821164 17.151740 8.727259 4.294724 1.688427 3.579221 6.040168 0.380987 1.052862 15.968989 0.926140 1.250376 10.457336 0.785678 1.371935 2.070646 1.335496 0.410573 0.639660 0.712449
min 0.000000 6.000000 3.700000 0.700000 0.300000 0.800000 25.100000 -1.000000 -2.700000 -38.000000 0.000000 0.000000 23.700000 0.000000 0.200000 0.300000 0.000000 0.000000 -7.100000 0.100000
25% 949.500000 51.000000 12.200000 4.200000 1.600000 3.700000 40.500000 0.000000 0.100000 8.500000 0.700000 1.000000 65.000000 0.500000 1.200000 1.800000 0.600000 0.400000 0.100000 0.700000
50% 1899.000000 63.000000 17.000000 6.400000 2.500000 5.500000 44.600000 0.300000 0.800000 19.400000 1.200000 1.700000 71.500000 0.900000 1.900000 2.800000 1.300000 0.600000 0.200000 1.100000
75% 2848.500000 74.000000 23.300000 9.400000 3.700000 8.100000 48.500000 0.500000 1.500000 30.250000 1.900000 2.600000 78.000000 1.500000 2.900000 4.300000 2.300000 0.900000 0.400000 1.600000
max 3798.000000 126.000000 68.000000 33.000000 13.400000 26.200000 74.600000 1.600000 4.300000 73.800000 7.800000 9.800000 127.100000 6.900000 12.000000 18.500000 9.000000 2.700000 14.800000 5.200000
In [19]:
df_main.plot(lw=0, marker=".", subplots=True, layout=(-1, 4),
          figsize=(15, 30), markersize=1);

3. Content investigation¶

In [20]:
# using matplotlib.pyplot - plt

# Plots the histogram for each numerical feature in a separate subplot
df_main.hist(bins=25, figsize=(15, 25), layout=(-1, 5), edgecolor="black")
plt.tight_layout();

4. Data Exploration¶

In [21]:
df_main['TARGET_5Yrs'].value_counts()
Out[21]:
1    6669
0    1331
Name: TARGET_5Yrs, dtype: int64
In [22]:
# using seaborn - sns

sns.set(style="white")
sns.set(style="whitegrid", color_codes=True)

sns.countplot(x = 'TARGET_5Yrs',
              data = df_main,
              palette = 'hls'
             )
plt.show()
plt.savefig('count_plot')

<Figure size 640x480 with 0 Axes>
In [26]:
count_lt_5Yrs = len(df_main[df_main['TARGET_5Yrs']==0])
count_gt_5Yrs = len(df_main[df_main['TARGET_5Yrs']==1])
pct_of_lt_5Yrs = count_lt_5Yrs/(count_lt_5Yrs+count_gt_5Yrs)
print("Percentage of NBA career less than 5 years is", pct_of_lt_5Yrs*100)
pct_of_gt_5Yrs = count_gt_5Yrs/(count_lt_5Yrs+count_gt_5Yrs)
print("Percentage of NBA career at least 5 years is", pct_of_gt_5Yrs*100)

Percentage of NBA career less than 5 years is 16.6375
Percentage of NBA career at least 5 years is 83.3625
In [27]:
df_main.groupby('TARGET_5Yrs').mean()
Out[27]:
Id Games Played Minutes Played Points Per Game Field Goals Made Field Goals Attempts Field Goals Percent 3Points Made 3Points Attempts 3Points Percent Free Throw Made Free Throw Attempts Free Throw Percent Offensive Rebounds Defensive Rebounds Rebounds Assists Steals Blocks Turnovers
TARGET_5Yrs
0 7764.149512 53.501127 14.932682 5.507739 2.111270 4.928325 42.420210 0.251615 0.808340 20.012697 1.032006 1.452968 70.445304 0.774305 1.644778 2.425620 1.330804 0.531555 0.063937 1.012923
1 7805.355676 64.629330 19.303929 7.618219 2.945899 6.491243 45.045719 0.267102 0.818204 19.498081 1.464477 2.046544 71.549543 1.138417 2.273024 3.408892 1.683131 0.672065 0.281392 1.306628
In [31]:
df_temp = df_main.drop(columns=['Id', 'TARGET_5Yrs'], axis=1)

sns.pairplot(df_temp, 
             height=1.5,
             plot_kws={"s":2, "alpha":0.2}
            );
In [29]:
# using matplotlib.pyplot - plt

# Plots the histogram for each numerical feature in a separate subplot
df_main.groupby('TARGET_5Yrs').hist(bins=25, figsize=(15, 25), layout=(-1, 5), edgecolor="black")
plt.tight_layout();
In [ ]: